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Found 5 papers, 5 papers with code
Learning Invariance from Generated Variance for Unsupervised Person Re-identification
This work focuses on unsupervised representation learning in person re-identification (ReID).
Unsupervised Lifelong Person Re-identification via Contrastive Rehearsal
Existing unsupervised person re-identification (ReID) methods focus on adapting a model trained on a source domain to a fixed target domain.
Unsupervised Domain Adaptation
Unsupervised Person Re-Identification
ICE: Inter-instance Contrastive Encoding for Unsupervised Person Re-identification
Then, we use similarity scores as soft pseudo labels to enhance the consistency between augmented and original views, which makes our model more robust to augmentation perturbations.
Joint Generative and Contrastive Learning for Unsupervised Person Re-identification
In this context, we propose a mesh-based view generator.
Enhancing Diversity in Teacher-Student Networks via Asymmetric branches for Unsupervised Person Re-identification
The objective of unsupervised person re-identification (Re-ID) is to learn discriminative features without labor-intensive identity annotations.
Unsupervised Domain Adaptation
Unsupervised Person Re-Identification
